Online load balancing and network flow

1993 ◽  
Author(s):  
Steven Phillips ◽  
Jeffery Westbrook
Keyword(s):  
Load Balancing ◽  
Network Flow

scholarly journals Advanced Load Balancing Based on Network Flow Approach in LTE-A Heterogeneous Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shucong Jia ◽  
Wenyu Li ◽  
Xiang Zhang ◽  
Yu Liu ◽  
Xinyu Gu
Keyword(s):  
Load Balancing ◽  
Heterogeneous Network ◽  
Network Flow ◽  
Negative Impact ◽  
Optimal Solution ◽  
System Level ◽  
Network Resource ◽  
System Level Simulation ◽  
Distribution Index ◽  
Unbalanced Load

Long-term evolution advanced (LTE-A) systems will offer better service to users by applying advanced physical layer transmission techniques and utilizing wider bandwidth. To further improve service quality, low power nodes are overlaid within a macro network, creating what is referred to as a heterogeneous network. However, load imbalance among cells often decreases the network resource utilization ratio and consequently reduces the user experience level. Load balancing (LB) is an indispensable function in LTE-A self-organized network (SON) to efficiently accommodate the imbalance in traffic. In this paper, we firstly evaluate the negative impact of unbalanced load among cells through Markovian model. Secondly, we formulate LB as an optimization problem which is solved using network flow approach. Furthermore, a novel algorithm named optimal solution-based LB (OSLB) is proposed. The proposed OSLB algorithm is shown to be effective in providing up to 20% gain in load distribution index (LDI) by a system-level simulation.

scholarly journals Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 162
Author(s):  
Sangho Yeo ◽  
Ye Naing ◽  
Taeha Kim ◽  
Sangyoon Oh
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Network Flow ◽  
Load Distribution ◽  
Linear Optimization ◽  
Flow Modeling ◽  
Selection Scheme ◽  
The Neural Network ◽  
Distributed Controllers ◽  
Switch Migration

Distributed controllers in software-defined networking (SDN) become a promising approach because of their scalable and reliable deployments in current SDN environments. Since the network traffic varies with time and space, a static mapping between switches and controllers causes uneven load distribution among controllers. Dynamic migration of switches methods can provide a balanced load distribution between SDN controllers. Recently, existing reinforcement learning (RL) methods for dynamic switch migration such as MARVEL are modeling the load balancing of each controller as linear optimization. Even if it is widely used for network flow modeling, this type of linear optimization is not well fitted to the real-world workload of SDN controllers because correlations between resource types are unexpectedly and continuously changed. Consequently, using the linear model for resource utilization makes it difficult to distinguish which resource types are currently overloaded. In addition, this yields a high time cost. In this paper, we propose a reinforcement learning-based switch and controller selection scheme for switch migration, switch-aware reinforcement learning load balancing (SAR-LB). SAR-LB uses the utilization ratio of various resource types in both controllers and switches as the inputs of the neural network. It also considers switches as RL agents to reduce the action space of learning, while it considers all cases of migrations. Our experimental results show that SAR-LB achieved better (close to the even) load distribution among SDN controllers because of the accurate decision-making of switch migration. The proposed scheme achieves better normalized standard deviation among distributed SDN controllers than existing schemes by up to 34%.

scholarly journals Load Balancing in Software Defined Networking: A Review

2018 ◽  
Vol 7 (2) ◽  
pp. 1-5
Author(s):  
Prabhjot Kaur ◽  
Jasmeen Kaur Chahal ◽  
Abhinav Bhandari
Keyword(s):  
Load Balancing ◽  
Network Flow ◽  
Software Defined Networking ◽  
Centralized Control ◽  
Control Plane ◽  
Survey Paper ◽  
Data Rates ◽  
Load Balancer ◽  
Service Data ◽  
And Control

Software Defined Networking is an adaptable way of networking, which disconnects data forwarding plane and control-plane of system equipment’s and also solves issues in existing network infrastructure. More specifically, the control-plane of software defined network decides the advancing way of network flow with Centralized Control Manner (CCM). SDN (Software Defined Networking) is a strategy for making, planning and overseeing systems which intend to change this present unfortunate circumstance. It has been used in dissimilar areas, like a campus networks and data center systems. In this survey paper, we’ve reviewed the concept of (SDNs) Software Defined Networks, its architecture and applications. In the survey, it has been found that SDN load balancing has become more smart and efficient and reduces the statistic collection overhead and maintain better QoS (Quality of Service) data rates. In addition, we reviewed the direct routing based algorithms of Load Balancer and compare with Round Robin Strategy. Furthermore, we’ve reviewed and compared the existing work to get better idea about the concept of Load balancing.

On-Line Load Balancing and Network Flow

Algorithmica ◽  
1998 ◽  
Vol 21 (3) ◽  
pp. 245-261 ◽  
Author(s):  
S. Phillips ◽  
J. Westbrook
Keyword(s):  
Load Balancing ◽  
Network Flow ◽  
Line Load ◽  
On Line

Power generation scheduling through use of generalised network flow programming

1991 ◽  
Vol 138 (1) ◽  
pp. 39 ◽  
Author(s):  
R.E. Rice ◽  
W.M. Grady ◽  
W.G. Lesso ◽  
A.H. Noyola ◽  
M.E. Connolly
Keyword(s):  
Power Generation ◽  
Network Flow ◽  
Generation Scheduling ◽  
Network Flow Programming

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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